OpenRaiser/Intern-Atlas huggingface dataset:
Who Contributes to the Knowledge Sharing Economy?
Venue: arXiv cs.SI 2015
Year: 2015

Information sharing dynamics of social networks rely on a small set of influencers to effectively reach a large audience. Our recent results and observations demonstrate that the shape and identity of this elite, especially those contributing \emph{original} content, is difficult to predict. Information acquisition is often cited as an example of a public good. However, this emerging and powerful theory has yet to provably offer qualitative insights on how specialization of users into active and passive participants occurs. This paper bridges, for the first time, the theory of public goods and the analysis of diffusion in social media. We introduce a non-linear model of \emph{perishable} public goods, leveraging new observations about sharing of media sources. The primary contribution of this work is to show that \emph{shelf time}, which characterizes the rate at which content get renewed, is a critical factor in audience participation. Our model proves a fundamental \emph{dichotomy} in information diffusion: While short-lived content has simple and predictable diffusion, long-lived content has complex specialization. This occurs even when all information seekers are \emph{ex ante} identical and could be a contributing factor to the difficulty of predicting social network participation and evolution.
The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks
Venue: arXiv cs.AI 2015
Year: 2015

We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models. As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them. Events attached by a semantic meaning do not need to have an explanation in terms of cause and effect.
Real-time Top-K Predictive Query Processing over Event Streams
Venue: arXiv cs.DB 2015
Year: 2015

This paper addresses the problem of predicting the k events that are most likely to occur next, over historical real-time event streams. Existing approaches to causal prediction queries have a number of limitations. First, they exhaustively search over an acyclic causal network to find the most likely k effect events; however, data from real event streams frequently reflect cyclic causality. Second, they contain conservative assumptions intended to exclude all possible non-causal links in the causal network; it leads to the omission of many less-frequent but important causal links. We overcome these limitations by proposing a novel event precedence model and a run-time causal inference mechanism. The event precedence model constructs a first order absorbing Markov chain incrementally over event streams, where an edge between two events signifies a temporal precedence relationship between them, which is a necessary condition for causality. Then, the run-time causal inference mechanism learns causal relationships dynamically during query processing. This is done by removing some of the temporal precedence relationships that do not exhibit causality in the presence of other events in the event precedence model. This paper presents two query processing algorithms -- one performs exhaustive search on the model and the other performs a more efficient reduced search with early termination. Experiments using two real datasets (cascading blackouts in power systems and web page views) verify the effectiveness of the probabilistic top-k prediction queries and the efficiency of the algorithms. Specifically, the reduced search algorithm reduced runtime, relative to exhaustive search, by 25-80% (depending on the application) with only a small reduction in accuracy.
Analisis dan Perancangan Wireless Roaming (Studi Kasus Universitas Baturaja)
Venue: arXiv cs.CY 2015
Year: 2015

Wireless roaming is one way to improve the reliability of a network of hotspots that are still using the topology Basic Service Set (BSS). When the user walks away from one access point (AP) or one AP die then begins to lose the signal, the mobile station (MS) is automatically connected with the AP to another without reconfiguring. Devices that support wireless roaming is the AP TP-Link TL-WR740N using DD-WRT firmware that supports DHCP forwarder. Wireless roaming makes it easy for the user if there is more than one AP in an area.
Mining Combined Causes in Large Data Sets
Venue: arXiv cs.AI 2015
Year: 2015

In recent years, many methods have been developed for detecting causal relationships in observational data. Some of them have the potential to tackle large data sets. However, these methods fail to discover a combined cause, i.e. a multi-factor cause consisting of two or more component variables which individually are not causes. A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables. In this paper, we propose a novel approach to address this practical causal discovery problem, i.e. mining combined causes in large data sets. The experiments with both synthetic and real world data sets show that the proposed method can obtain high-quality causal discoveries with a high computational efficiency.
Parallel Dither and Dropout for Regularising Deep Neural Networks
Venue: arXiv cs.LG 2015
Year: 2015

Effective regularisation during training can mean the difference between success and failure for deep neural networks. Recently, dither has been suggested as alternative to dropout for regularisation during batch-averaged stochastic gradient descent (SGD). In this article, we show that these methods fail without batch averaging and we introduce a new, parallel regularisation method that may be used without batch averaging. Our results for parallel-regularised non-batch-SGD are substantially better than what is possible with batch-SGD. Furthermore, our results demonstrate that dither and dropout are complimentary.
Mining Open Datasets for Transparency in Taxi Transport in Metropolitan Environments
Venue: arXiv cs.CY 2015
Year: 2015

Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban commuters, we provide a direct price comparison between Uber and the Yellow Cab company in New York. We discover that Uber, despite its lower standard pricing rates, effectively charges higher fares on average, especially during short in length, but frequent in occurrence, taxi journeys. Building on this insight, we develop a smartphone application, OpenStreetCab, that offers a personalized consultation to mobile users on which taxi provider is cheaper for their journey. Almost five months after its launch, the app has attracted more than three thousand users in a single city. Their journey queries have provided additional insights on the potential savings similar technologies can have for urban commuters, with a highlight being that on average, a user in New York saves 6 U.S. Dollars per taxi journey if they pick the cheapest taxi provider. We run extensive experiments to show how Uber's surge pricing is the driving factor of higher journey prices and therefore higher potential savings for our application's users. Finally, motivated by the observation that Uber's surge pricing is occurring more frequently that intuitively expected, we formulate a prediction task where the aim becomes to predict a geographic area's tendency to surge. Using exogenous to Uber datasets we show how it is possible to estimate customer demand within an area, and by extension surge pricing, with high accuracy.
Filtered-OFDM - Enabler for Flexible Waveform in The 5th Generation Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

The underlying waveform has always been a shaping factor for each generation of the cellular networks, such as orthogonal frequency division multiplexing (OFDM) for the 4th generation cellular networks (4G). To meet the diversified and pronounced expectations upon the upcoming 5G cellular networks, here we present an enabler for flexible waveform configuration, named as filtered-OFDM (f-OFDM). With the conventional OFDM, a unified numerology is applied across the bandwidth provided, balancing among the channel characteristics and the service requirements, and the spectrum efficiency is limited by the compromise we made. In contrast, with f-OFDM, the assigned bandwidth is split up into several subbands, and different types of services are accommodated in different subbands with the most suitable waveform and numerology, leading to an improved spectrum utilization. After outlining the general framework of f-OFDM, several important design aspects are also discussed, including filter design and guard tone arrangement. In addition, an extensive comparison among the existing 5G waveform candidates is also included to illustrate the advantages of f-OFDM. Our simulations indicate that, in a specific scenario with four distinct types of services, f-OFDM provides up to 46% of throughput gains over the conventional OFDM scheme.
Mixed Covering Arrays on 3-Uniform Hypergraphs
Venue: arXiv cs.DM 2015
Year: 2015

Covering arrays are combinatorial objects that have been successfully applied in the design of test suites for testing systems such as software, circuits and networks, where failures can be caused by the interaction between their parameters. In this paper, we perform a new generalization of covering arrays called covering arrays on 3-uniform hypergraphs. Let $n, k$ be positive integers with $k\geq 3$. Three vectors $x\in \mathbb Z_{g_1}^n$, $y\in \mathbb Z_{g_2}^n$, $z\in \mathbb Z_{g_3}^n$ are {\it 3-qualitatively independent} if for any triplet $(a, b, c) \in \mathbb Z_{g_1}\,\times\, \mathbb Z_{g_2}\,\times\,\mathbb Z_{g_3}$, there exists an index $ j\in \lbrace 1, 2,...,n \rbrace $ such that $( x(j), y(j), z(j)) = (a, b, c)$. Let $H$ be a 3-uniform hypergraph with $k$ vertices $v_1,v_2,\ldots,v_k$ with respective vertex weights $g_1,g_2,\ldots,g_k$. A mixed covering array on $H$, denoted by $3-CA(n,H, \prod_{i=1}^{k}g_{i})$, is a $k\times n$ array such that row $i$ corresponds to vertex $v_i$, entries in row $i$ are from $Z_{g_i}$; and if $\{v_x,v_y,v_z\}$ is a hyperedge in $H$, then the rows $x,y,z$ are 3-qualitatively independent. The parameter $n$ is called the size of the array. Given a weighted 3-uniform hypergraph $H$, a mixed covering array on $H$ with minimum size is called optimal. We outline necessary background in the theory of hypergraphs that is relevant to the study of covering arrays on hypergraphs. In this article, we introduce five basic hypergraph operations to construct optimal mixed covering arrays on hypergraphs. Using these operations, we provide constructions for optimal mixed covering arrays on $α$-acyclic 3-uniform hypergraphs, conformal 3-uniform hypertrees having a binary tree as host tree, and on some specific 3-uniform cycle hypergraphs.
A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment
Venue: arXiv cs.IT 2015
Year: 2015

The application of multiple-input multiple-output (MIMO) techniques to non-orthogonal multiple access (NOMA) systems is important to enhance the performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for downlink and uplink transmission is proposed by applying the concept of signal alignment. By using stochastic geometry, closed-form analytical results are developed to facilitate the performance evaluation of the proposed framework for randomly deployed users and interferers. The impact of different power allocation strategies, such as fixed power allocation and cognitive radio inspired power allocation, on the performance of MIMO-NOMA is also investigated. Computer simulation results are provided to demonstrate the performance of the proposed framework and the accuracy of the developed analytical results.
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
Venue: arXiv cs.NE 2015
Year: 2015

Learning Classifier Systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a Genetic Algorithm (GA) to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding "macro-actions," created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task.
On the Ergodic Capacity of Underlay Cognitive Dual-Hop AF Relayed Systems under Non-Identical Generalized-K Fading Channels
Venue: arXiv cs.IT 2015
Year: 2015

The ergodic capacity of underlay cognitive (secondary) dual-hop relaying systems is analytically investigated. Specifically, the amplify-and-forward transmission protocol is considered, while the received signals undergo multipath fading and shadowing with non-identical statistics. To efficiently describe this composite type of fading, the well-known generalized-$K$ fading model is used. New analytical expressions and quite accurate closed-form approximations regarding the ergodic capacity of the end-to-end communication are obtained, in terms of finite sum series of the Meijer's-$G$ function. The analytical results are verified with the aid of computer simulations, while useful insights are revealed.
Performance Analysis of Split Preamble RAN Over-load Protocol for M2M Communications in Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

Machine type communications (MTC) in 3G/4G networks is getting more attention recently due to bursty nature of traffic characteristics in contrast to Poisson type H2H traffic. A large number of methods have been suggested in the literature. In this paper we give a mathematical model on performance analysis of Disjoint Allocation (DA) and Joint Allocation (JA) methods for allocating preambles to M2M and H2H users. In an earlier work we had investigated the performance of two possible splitting preamble methods on collision probability and energy reduction for MTC subscribers. In this paper we develop a stochastic model for JA/DA method in RACH procedure using a $K$th order Markov chain approach and carry out performance analysis in terms of collision probability, access success probability, average access delay, statistics of preamble transmissions and statistics of access delay for M2M users. The optimal number of reserved preamble set is derived based on the given success access delay threshold. Numerical results verifies the simulation results and demonstrates that this model can estimate the performance of the JA/DA method accurately.
Non-negative submodular stochastic probing via stochastic contention resolution schemes
Venue: arXiv cs.DS 2015
Year: 2015

The abstract model of stochastic probing was presented by Gupta and Nagarajan (IPCO'13), and provides a unified view of a number of problems. Adamczyk, Sviridenko, Ward (STACS'14) gave better approximation for matroid environments and linear objectives. At the same time this method was easily extendable to settings, where the objective function was monotone submodular. However, the case of non-negative submodular function could not be handled by previous techniques. In this paper we address this problem, and our results are twofold. First, we adapt the notion of contention resolution schemes of Chekuri, Vondrák, Zenklusen (SICOMP'14) to show that we can optimize non-negative submodular functions in this setting with a constant factor loss with respect to the deterministic setting. Second, we show a new contention resolution scheme for transversal matroids, which yields better approximations in the stochastic probing setting than the previously known tools. The rounding procedure underlying the scheme can be of independent interest --- Bansal, Gupta, Li, Mestre, Nagarajan, Rudra (Algorithmica'12) gave two seemingly different algorithms for stochastic matching and stochastic $k$-set packing problems with two different analyses, but we show that our single technique can be used to analyze both their algorithms.
Recovery guarantees for multifrequency chirp waveforms in compressed radar sensing
Venue: arXiv cs.IT 2015
Year: 2015

Radar imaging systems transmit modulated wideband waveform to achieve high range resolution resulting in high sampling rates at the receiver proportional to the bandwidth of the transmit waveform. Analog processing techniques can be used on receive to reduce the number of measurements to N, the number of potential delay bins. If the scene interrogated by the radar is assumed to be sparse consisting of K point targets, results from compressive sensing suggest that number of measurements can be further reduced to scale with K logN for stable recovery of a sparse scene from measurements with additive noise. While unstructured random projectors guarantee successful recovery under sparsity constraints, they cannot be implemented in the radar hardware in practice. Recently, structured random Toeplitz and Circulant matrices that result from using stochastic waveforms in time delay estimation setting have been shown to yield recovery guarantees similar to unstructured sensing matrices. However, the corresponding transmitter and receiver structures have high complexity and large storage requirements. In this paper, we propose an alternative low complexity compressive wideband radar sensor which combines multitone signal chirp waveform on transmit with a receiver that utilizes an analog mixer followed with a uniform sub-Nyquist sampling stage. We derive the recovery guarantees for the resulting structured measurement matrix and sufficient conditions for the number of tones. The only random component of our design is the sparse tone spectrum implementable efficiently in hardware. Our analytical and empirical results show that the performance of our scheme is in par with unstructured random sensing matrices and structured Toeplitz and Circulant matrices with random entries.
A Comparison of High-Level Design Tools for SoC-FPGA on Disparity Map Calculation Example
Venue: arXiv cs.OH 2015
Year: 2015

Modern SoC-FPGA that consists of FPGA with embedded ARM cores is being popularized as an embedded vision system platform. However, the design approach of SoC-FPGA applications still follows traditional hardware-software separate workflow, which becomes the barrier of rapid product design and iteration on SoC-FPGA. High-Level Synthesis (HLS) and OpenCL-based system-level design approaches provide programmers the possibility to design SoC-FGPA at system-level with an unified development environment for both hardware and software. To evaluate the feasibility of high-level design approach especially for embedded vision applications, Vivado HLS and Altera SDK for OpenCL, representative and most popular commercial tools in market, are selected as evaluation design tools, disparity map calculation as targeting application. In this paper, hardware accelerators of disparity map calculation are designed with both tools and implemented on Zedboard and SoCKit development board, respectively. Comparisons between design tools are made in aspects of supporting directives, accelerator design process, and generated hardware performance. The results show that both tools can generate efficient hardware for disparity map calculation application with much less developing time. Moreover, we can also state that, more directives (e.g., interface type, array reshape, resource type specification) are supported, but more hardware knowledge is required, in Vivado HLS. In contrast, Altera SDK for OpenCL is relatively easier for software programmers who is new to hardware, but with the price of more resources usage on FPGA for similar hardware accelerator generation.
Compromising a Medical Mannequin
Venue: arXiv cs.CY 2015
Year: 2015

Medical training devices are becoming increasingly dependent on technology, creating opportunities that are inherently conducive to security breaches. Previous medical device research has focused on individual device security breaches and the technical aspects involved with these breaches. This research examines the viability of breaching a production-deployed medical training mannequin. The results of the proof of concept research indicate that it is possible to breach a medical training mannequin in a live environment. The research contribution is an initial empirical analysis of the viability of compromising a medical training mannequin along with providing the foundation for future research.
Bounds and Fixed-Parameter Algorithms for Weighted Improper Coloring (Extended Version)
Venue: arXiv cs.DM 2015
Year: 2015

We study the weighted improper coloring problem, a generalization of defective coloring. We present some hardness results and in particular we show that weighted improper coloring is not fixed-parameter tractable when parameterized by pathwidth. We generalize bounds for defective coloring to weighted improper coloring and give a bound for weighted improper coloring in terms of the sum of edge weights. Finally we give fixed-parameter algorithms for weighted improper coloring both when parameterized by treewidth and maximum degree and when parameterized by treewidth and precision of edge weights. In particular, we obtain a linear-time algorithm for weighted improper coloring of interval graphs of bounded degree.
Fingerprinting-Based Positioning in Distributed Massive MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing. Although there are many available positioning techniques, none of them is adapted to work with massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G technology candidate. In this paper, we discuss possible solutions for positioning of mobile stations using a vector of signals at the base station, equipped with many antennas distributed over deployment area. Our main proposal is to use fingerprinting techniques based on a vector of received signal strengths. This kind of methods are able to work in highly-cluttered multipath environments, and require just one base station, in contrast to standard range-based and angle-based techniques. We also provide a solution for fingerprinting-based positioning based on Gaussian process regression, and discuss main applications and challenges.